Machine Learning and Blockchain – Part 3/3 – Future Scenarios and Business Ideas

Published by Martin Schuster on

This third part of our Blog series about machine learning and blockchain will cover potential future scenarios where machine learning could be used in connection with the blockchain. These scenarios are not necessarily all feasible but should give you an idea how new business models could be developed by incorporating machine learning and the blockchain. These ideas are not exclusively connected to one or the other machine learning variant and can often be used for multiple of these variants.

Decentralized data marketplaces:

A decentralized data marketplace is a platform that enables data owners to share their data on a blockchain network with other parties who can access and use the data for unsupervised learning tasks. This approach allows for the creation of a transparent and secure marketplace that facilitates data exchange without relying on a centralized intermediary.

In this type of marketplace, data owners can upload their data onto the blockchain and set the terms of access, including the price and conditions of use. Data buyers can then browse the available data sets, purchase the ones that meet their needs, and access the data via the blockchain network. The blockchain ensures that data ownership and access rights are securely, and transparently managed, and smart contracts can be used to automate payment and ensure that data owners receive compensation for their contributions.

Unsupervised learning, in this context, refers to machine learning algorithms that are designed to identify patterns and relationships within a data set without the need for labeled data. By using unsupervised learning, data buyers can identify insights and relationships that may not be apparent from labeled data, allowing them to develop more accurate and robust models.

One of the key benefits of a decentralized data marketplace is that it provides a secure and transparent platform for data exchange, without the need for a centralized intermediary. This can help to reduce the risk of data breaches and ensure that data owners retain control over their data. Additionally, the use of blockchain technology can help to ensure that data is properly authenticated and verified, helping to increase trust and confidence in the data.

Businesses and organizations looking to access and leverage large data sets can benefit from a decentralized data marketplace based on unsupervised learning and blockchain technology. While providing data owners with a secure and transparent platform for sharing their data and being rewarded for it.

Tokenized model marketplaces:

Tokenized model marketplaces combine two powerful technologies: supervised learning and blockchain, to create a new way for model trainers to monetize their work and for model users to access high-quality models.

Supervised learning is a type of machine learning where a computer program is trained on a labeled dataset to make predictions or recommendations. Model trainers use supervised learning algorithms to build models that can accurately predict future outcomes or recommend actions based on historical data.

Blockchain, on the other hand, is a decentralized and distributed ledger technology that allows for secure, transparent, and tamper-proof transactions between parties without the need for intermediaries.

In a tokenized model marketplace, model trainers can tokenize their models and list them on a blockchain-based marketplace. Each model is represented by a unique token that can be bought and sold on the marketplace using cryptocurrencies or tokens. The model trainer sets the price for the model, and when a model user buys the token, they gain access to the model.

The benefits of tokenized model marketplaces are many. For model trainers, they can monetize their work by selling their models directly to model users. They can also receive ongoing revenue by setting a royalty fee for each time the model is used by a model user.

For model users, they have access to a wide variety of high-quality models that they can use for their business needs. They can also be assured of the quality of the models because the marketplace can use smart contracts to ensure that the models meet certain performance standards.

Tokenized model marketplaces have the potential to disrupt the traditional model training industry and provide a more efficient and transparent way for model trainers and model users to interact with each other.

Decentralized Autonomous Organizations (DAOs):

A Decentralized Autonomous Organization (DAO) is a type of organization that operates on a blockchain network and is managed by smart contracts. These smart contracts are self-executing programs that can perform certain tasks automatically without the need for human intervention.

In a DAO, decision-making is decentralized and made based on the collective preferences of its members. These members can be humans, AI agents, or other DAOs, and they can participate in decision-making through a process called “governance.”

Reinforcement learning can be used in DAOs to help them make decisions based on the preferences of their members. Reinforcement learning is a type of machine learning that uses trial and error to learn how to make decisions based on a reward system. In the context of a DAO, reinforcement learning can help the organization learn which decisions are most likely to be approved by its members and can adjust its decision-making process accordingly.

For example, a DAO could be created for a group of investors who want to collectively invest in a portfolio of assets. The DAO could use reinforcement learning to identify the assets that are most likely to generate the highest returns based on the preferences of its members. The DAO could then use smart contracts to automatically execute trades and manage the portfolio.

The use of reinforcement learning with blockchain technology can enable the creation of highly efficient and effective decentralized, autonomous organizations that can operate with minimal human intervention while still being able to make decisions based on the preferences of their members.

Collaborative learning:

Collaborative learning is a method of training machine learning models using data from multiple sources. However, in traditional collaborative learning approaches, the local data from each party needs to be shared with others, which can lead to privacy and security issues.

Deep learning models can be trained using a technique called Federated Learning, where local models are trained on the parties’ data, and only the model updates are sent to a central server. However, even in Federated Learning, there are potential security vulnerabilities and the need for a trusted central authority.

By leveraging blockchain technology, parties can jointly train a deep learning model without sharing their local data. Each party can contribute their training data to the blockchain network, and the network can use a consensus mechanism to securely aggregate and update model weights and gradients.

Here’s how it could work:

  1. Parties with data to contribute to the model join the blockchain network.
  2. Each party trains a local model on their data and sends model updates (weights and gradients) to the blockchain network.
  3. The network aggregates the model updates and computes a new global model update that reflects all the parties’ data.
  4. The updated global model is sent back to each party, which can then use it to train their local model further.

Steps 2-4 are repeated iteratively until the desired level of model accuracy is achieved.

This approach provides a secure and privacy-preserving way to collaboratively train a deep learning model, without the need for a trusted central authority. The blockchain network ensures that all model updates are authentic and that no party can tamper with the model or the updates.

As a business idea, this approach could be applied to various industries where parties need to collaborate on training machine learning models without compromising the privacy and security of their data. For example, healthcare providers could use this approach to train medical image analysis models collaboratively, or financial institutions could collaborate on fraud detection models. The business could provide a platform that facilitates the creation and management of such collaborations on the blockchain network, charging fees for using the platform and offering additional services such as model evaluation and selection.


The concept involves using transfer learning and blockchain technology to enable multiple financial institutions to share their pre-trained fraud detection or risk assessment models on a blockchain network. By doing this, the models can be reviewed and updated in a transparent and decentralized manner.

Transfer learning is a machine learning technique where a pre-trained model is used as a starting point for another model, which can then be fine-tuned for a specific task. In this case, the pre-trained models are used for fraud detection or risk assessment, and multiple financial institutions can share their pre-trained models on a blockchain network.

Blockchain technology can provide a secure and decentralized platform for sharing these models. Each financial institution can maintain control over their model while also being able to share it with others. By sharing the models, financial institutions can benefit from the collective knowledge and expertise of the group.

Moreover, blockchain can enable transparent and auditable updates to the models. Any updates made to the models can be recorded on the blockchain, allowing for easy review and verification by other financial institutions. This can help to improve the accuracy and effectiveness of the models.

In conclusion, the business idea involves using transfer learning and blockchain to create a platform where financial institutions can share and collaborate on pre-trained fraud detection or risk assessment models. This can help to improve the efficiency and effectiveness of these models, while also ensuring transparency and security in the sharing and updating process.

This concludes the third and final part of our blog series about Machine Learning and Blockchain. We hope you got a good understanding of what machine learning is, how it works and how it is used and potentially could be used in the future.



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